INTRODUCTION

Agriculture, in most developing countries, is both the traditional pursuit and the key to the sustained modern economic growth. The growth of the economy without doubts conjoint with agriculture progress. The contribution of agriculture to economic growth according to Oyakhilomen et al (2014) is in:

The production of foods and raw materials is a necessary thing for the stimulation and development of all other sectors of every national economy, that is to say, the development of the agricultural sector positively affects every other sector of the national economic growth Oladimeji (2004). As a sector that focuses on primary production, agriculture itself has to be technological and innovative ori-ented in order to enhance the productivity of the sector (Nyienakuna 2010). According to (Mhlanga, 2010), the agricultural sector in Nigeria consists of little competition, large and growing demand for food and beverage products and favorable environmental conditions. With the trade deficit in agricultural products where demands continue to rise, it creates immense opportunity for those willing to invest in revitalizing the sector. Although, the sector is yet to reach its potential production level with crop and livestock production remaining below demand, not enough to support the ever-growing population. As a result, Nigeria once a net exporter of food has now become a net importer spending over $4.2billion on food in 2010 with import rising exponentially to feed the growing population. For instance, with Nigeria’s current fish production output estimated at 0.5 million tons, its true potential is 2.5 million tons annually with opportunities for investments going beyond direct production. Certain drawbacks show imbalances and maladjustment resulting from uneven progress in the growth and development from agriculture to agribusiness, for example, glutted markets, unstable processes, uneconomic farm units, poor managerial training and lack of broadly conceived agribusiness policy (M.C Igbokwuwe et al 2015).

Agriculture remains one of the most dynamic and critical sectors in the Nigerian economy. It employs 70% of Nigerians, including many rural women, and contributes up to 22.86 % of the country’s GDP. As in many other African countries, agriculture in Nigeria largely depends on food crops for the home market, given the Nigerian population estimated at 196 million people. In spite of this reality, Nigeria remains a net importer of food, for many reasons. First of all, the majority of the agriculture-focused operations in the country are small-scale, with limited innovation regarding inputs, harvesting, processing, distribution, and access to markets (NBS, 2018). The system and characteristics of farm agribusiness linkages in Nigeria can best be understood within the context of Nigeria's economic situation; obviously, agriculture plays the primary role of supply of raw materials to the agro-industrial processing and manufacturing sector (FAO 2010). Furthermore, it facilitates the other basic functions of agriculture such as food supply, provision of employment opportunities and income generation as well as contribution to foreign exchange earnings through exports; In Nigeria, the pace of accomplishing synergy between agriculture and industrial sector has persistently been very slow. This is partly because of the frequent changes in policy beginning with the import substitution strategies of the pre-1986 era that discouraged industrialists from patronizing locally produced raw materials (FAO 2010).

Overview of Nigeria Agriculture

In Nigeria, there are over 30 million hectares of farmland under cultivation season to season, falling substantially short of the estimated 78.5 million hectares of land that is required for farming to feed Nigeria’s growing population. The country’s agricultural sector is dominated by smallholder farmers who work an average of 4–5 acres each, under rain-fed conditions (NIRSAL, 2017). Most of them lack knowledge of modern practices, have insufficient capital and own little or no equipment of their own. As a result, much of the farm machinery, seeds, and chemicals they require are purchased and distributed to them by the government under various agricultural assistance/subsidy programs. One of such subsidy packages is the e-Wallet system introduced in 2014 under the Agricultural Transformation Agenda (ATA) through which subsidized electronic vouchers for inputs were delivered directly to the farmers' mobile phones and then the vouchers were used as cash to purchase the inputs directly from agrodealers nearest to them (Obayelu 2017). This initiative has proven to be quite efficient and effective in providing poor farmers with much-needed resources and eliminating corruption in the subsidy program (Charles D 2004). There are about ten relatively large commercial scale farms in Nigeria deploying some form of mechanized processes, these and a few construction companies can purchase tractors for agricultural and non-agricultural purposes(APP 2016).

During the Structural Adjustment years, the government encouraged backward integration, but inconsistencies in macroeconomic policy initiatives between 1986 and 1995 discouraged farmers from expanding production of suitable agricultural raw materials for local processing and manufacturing (FAO 2004). Backward integration and the privatization of state-owned enterprises are currently emphasized as a desirable policy objective by the new democratic government but growth in the manufacturing and agribusiness sector has changed very little in the past decade, Between 1990 and 1999, manufacturing including agro-industrial output in real terms actually dropped to about 92 percent of the level reached in 1990; Whilst the other productive sectors did register modest growth, this was more than offset – in terms of per capita output – by the annual increase in population (FAO 2004). The one exception was agriculture, there were no significant changes in the structure of output over the decade in Nigeria; in 1999, agriculture accounted for nearly 40 percent of GDP, virtually the same proportion as in 1990; the average share of output originating from manufacturing declined from eight to nine percent (FAO 2004). The crude oil sector share of GDP accounted for 12.7 percent in 1999, virtually the same as nine years ago;

Without the adequate capacity to effectively engage in several aspects of agricultural innovation, transforming agriculture in sub-Saharan Africa will be a long, drawn-out process; the development of the agricultural sector will also require a different set of skills in order for agribusiness institutions to adjust and expand in order to become competitive in the regional and international markets, agribusiness firms in sub-Sahara Africa (Nigeria) need sufficiently and appropriately trained human capital (World Bank, 2013). Similarly, Francis, (2017) attributed the reason why most agribusinesses fail in Nigeria to the inability of farm owners to pay attention to managerial skills needed for sustaining such business. The survival rate of agribusiness in Rivers State Nigeria is low due to factors which majority of the agribusiness operators attributed to poor capital. Survival rates of Agribusinesses' output are low mostly after the first year due to a number of factors that sometimes result in borrowing so as to obtain sufficient finance to facilitate their early stages of growth. Asma et al (2015). The important question begging for answer is why the low rate of agribusiness survival in Rivers State, Nigeria? What is the level of managerial and technological capacities of agribusiness operators? It is against this backdrop that this study is being designed to enable us to determine those factors that affect the survival of agribusinesses as well as examine the effectiveness of human and technological capacities development on agribusiness survival in Rivers State.

DATA AND MODEL

Data Collection

This study adopted a cross-sectional research design. In order to collect as much relevant information on the subject matter as possible, data was collected from both primary and secondary sources. Primary Sources: A set of well-structured questionnaire was used to gather information from agribusiness operators (respondents) and a personal interview was conducted where necessary. Secondary data were also consulted in writing the thesis especially from sources such as Journals, conferences, seminars, textbooks as well as online sources. Information from these sources formed the basic core of the study. A total of 300 agribusiness operators within the two (2) L.G.A.s were randomly sampled for the survey, in each L.G.A, 150 agribusiness operators were sampled. The study relied heavily on primary data obtained from the operators.

Section A of the questionnaire was structured to obtain information on the socio-economic characteristics or variables of the respondents including gender, age, marital status, household size, educational qualification, income/ revenue level, years of business experience and the number of employees. The purpose is to extract information on the distinctive factors that affect the survival of agribusiness. Section B highlighted common agribusinesses that operate within the State as well as those possible factors/ variables that could affect the smooth operation of agribusiness. Section C of the questionnaire concentrated on how human and technological capacities development could affect agribusiness survival separately. It also pointed out relevant areas that require capacity development in order to for agribusiness to survival and also some constraint that is mitigating against the effective use of technology for the survival of agribusinesses.

Description of Study Area

This study was conducted in Obio/Akpor and Port Harcourt City Local Government Areas (L. G. As) in Rivers State Nigeria. Nigeria, a country located on the western coast of Africa. Nigeria has a diverse geography, with climates ranging from arid to humid equatorial. Rivers State also known as the treasure base of the nation, one of the 36 States of the Federation lies mainly within the Delta region of Rivers Niger. Rivers State is made up of 23 Local Government Areas (LGAs) and covers a land size of about 21,850 square km with a population of about 5,185,400 according to the 2006 census. The state is bordered in the North by Imo, Anambra and Abia State, in the East by Akwa Ibom State, in the West by Bayelsa and Delta State and in the South by the Atlantic Ocean.

Port Harcourt City Local Government Area is located in the heart of Rivers State where the industrial activities take place. It has a total size of 109 square kilometers (42 sq mi) and lies within Latitude: 4°51'N. Longitude: 7°0'E with a total population of 538,558 according to the 2006 population census. Port Harcourt features a tropical monsoon climate with lengthy and heavy rainy seasons and very short dry seasons. Port Harcourt’s heaviest precipitation is seen during the month of September where an average of approximately 370 mm of rain is seen, and average temperature typically between 250C -290C in the city.

Obio/Akpor is a local government area in the metropolis of Port Harcourt, one of the major centers of economic activities in Nigeria, and one of the major cities of the Niger Delta. The local government area covers 260 km² and at the 2006 Census held a population of 464,789. Obio/Akpor is located on latitudes 4°45'N and 4°60'N and longitudes 6°50'E and 8°00'E with its headquarters at Rumuodomaya.

Theoritical Models

Data obtained from the field survey were edited, coded and cleaned to ensure consistency, uniformity, and accuracy. The data collected were processed and analyzed with the use of SPSS version 22. In order to achieve objective one of this study i.e. to determine the factors that affect agribusiness as well as the technological constraint faced by agribusiness operators in Rivers State, descriptive statistics such as percentage and frequency were adopted. To achieve objectives two, that is, inadequate human and technological capacities whether they affect agribusiness in Rivers State. Factor, cluster, and multinomial logistic regression analysis.

Factor Analysis:

Based on Chua's (2009) suggestion, factor analysis is the procedure used by many researchers to organize, identify and minimize big items from the questionnaire to certain constructs under one dependent variable in research. Kaiser- Meyer-Olkin (KMO) test was done to identify whether the data is suitable for factor analysis. The KMO test formula as stated by M. J Norusis (1994).

Where:

According to Johnson and Wichern (2002), the factor analysis model is as stated below:

Where,

Multinomial Logistic Regression Model

The logistic regression model is an extension of the binomial logistic regression model. It is used to predict a dependent variable given one or more independent variables. A multinomial logistic regression model was used to determine the extent to which the dependent variable (monthly net profit) can be predicted by the independent variables. Multinomial logit models are discrete choice models used for the case of a dependent variable with more than two categories (Gujarati and Sangeetha, 2007 and Greene, 2008). Perhaps the simplest approach to multinomial data is to nominate one of the response categories as a baseline or reference categories, calculate log-odds for all other categories relative to the baseline, and then let the log-odds be a linear function of the predictors. In the multinomial logit model, we assume that the log-odds of each response follow a linear model.

Where,

Note that we have written the constant explicitly, so we will assume henceforth that the model matrix X does not include a column of ones.

The factors that affect agribusiness, as well as technological constraints faced by agribusiness operators, are pre-sented in the descriptive form in table 1 below. Econometric/empirical results obtained from the factor analysis and a multinomial logistic regression model. The multinomial logistic regression model was used to examine whether inadequate human and technological capacities development affects agribusiness. The results are discussed below.

ESTIMATION AND RESULTS

Descriptive Statistics

The survey that was randomly carried-out among agribusiness operators, out of the 300 respondents interviewed to obtain the extent to which the above factors affect its agribusiness on the scale of “not very great extent to very great extent”, according to the respondents, 35.7% indicated that inadequate human capacity development affect them to a very great extent, 29.7% indicated great extent, 23% were neutral while not great extent and not very great extent shows 6.3% and 5.3 respectively. Another factor examined was inadequate capital and respondents indicated as follows, 50.7% affected to a very great extent, 31.7 to a great extent, 10.3% were indifferent, 6% says not great extent while 1.3% indicated not very great extent. The majority of the respondents indicated that to a very great extent inadequate capital affects their businesses. Inadequate technological capacity development, an important factor was also examined and the respondents indicated as follows, very great extent 33.3%, and great extent 26.3%, those who indicated neutral was 23.3%, not great extent category was 5.7% while not very great extent was 4.3%. The majority of the respondents (33.3%) were indifferent regarding inadequate technological capacity development. The reason given was a lack of information and the ability to apply the technology. As shown in <Table 1> all the factors examined were above 20% for both “great and very great extent” scale and below 10% for both “not great and not very great extent. This implies that these factors greatly affect agribusiness in Rivers State.

Sequel to the results of the survey, out of the 300 agribusiness operators interviewed on the extent to which these constraints affect its application of technology in its agribusinesses. As obtained from the survey, for high cost of purchasing the technology, 7.0% of the respondents stated that it is not to a very great extent, 6.3% indicated not great extent, 23% were neutral, meanwhile 32.0% and 32.0% revealed that high purchasing cost affect them to a great extent and very great extent respectively.

Another constraint was lack/inadequate technical knowhow. 39.3% of the respondents stated that this constraint affects them to a very great extent, 26.0% to a great extent, 25.3% were neither here nor there, while 5.3% of the respondents indicated not great extent, and the least was 4.0% responded to not very great extent. The table shows that the majority of the respondents don’t use technology due to the lack of/inadequate technical skills and knowledge required by agribusiness operators. 35.7% of the respondents were neutral, those who lack idea on how to source for the technology affect to a great extent constitute 29.0% while those who are a constraint to a very great extent were 19.7%. Those within the scale of not very great extent and not great extent were 7.7% and 8.0% respectively. This shows that the majority are indifferent about how to source for the technology why 29.0% second highest are a constraint to a great extent due to no knowledge on how to source for the technology that would improve their agribusinesses. The survey also shows that 33.0% of the respondents indicated that to a very great extent the high cost of maintenance is a constraint to them, while 31.3% were a constraint to a great extent. 25.3% were in a neutral position and 5.0% stated that it is not to a great extent while 5.3% of the respondents indicated not very great extent. A majority (43.0%) of the respondents revealed that they are a constraint to a very great extent, 27.7% were of great extent, 20.7% neutral while not great extent and not very great extent scale were of the same 4.3%. From the above table, more agribusiness operators would be willing to adopted and invest in the appropriate technology if the chances of success thereafter are high.

Empirical Results of Objective two

(1) Factors extraction and loading analysis

Factor analysis was employed to construct new factors (components) that affect agribusiness in the study area. Kaiser–Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s Test of Sphericity are both tests that can be used to determine the factorability of the matrix as a whole. Based on the results <Table 3> the Kaiser –Meyer- Olkin (KMO) measure of Sampling Adequacy is 0.894 which is greater than 0.6, this confirmed that the data set was fit to be use subjected to model for data reduction analysis Furthermore, the result value of Bartlett’s Test of Sphericity significant (P<.000).

Based on the results, it is ideal to proceed with the factor analysis

After the extraction or identification of the preferential factors in the first step, the next step was to subject the factors to rotated component matrix analysis. This was done with the aim of obtaining the covariance and correlations of the rotated factor loadings. The results obtained are presented below in <Table 4> Values below 0.50 were omitted from the loading and just those above 0.50 were retained to identify the loading under each factor.

After factor analysis, the following results are obtained. Factor 1 conclude: production, storage, packaging, processing, technology, post-harvest handling. Factor 2 conclude: marketing skills, bood and record-keeping, business planning/development, finance and credit management. Factor 3 conclude: finance and credit management, income, investment analysis. Factor 4 conclude: transportation, supply.

(2) Multi-collinearity test

The factor scores obtained from the factor loading were saved as variables and subjected to a multi-collinearity test to observe if any collinearity existed and if the data set was suitable for the multinomial logistic regression model chosen.

The variance inflation factors (VIF) values which are all less than 10 show the there are no perfect multicollinearity, <Table 6> shows, therefore, it is fit for the analysis.

The model tested significant (P<.000) at a 5% confidence interval, therefore, it was appropriate to use for the analysis of the factor scores. In addition, the VIF values of the selected variables that were subjected to analysis were all within an acceptable range.

(4) Result of Multinomial Logistic Regression Analysis:

Null Hypothesis (H₀): There is no significant relationship between inadequate human & technological capacities development and agribusiness.

Alternative Hypothesis (Ha): There is a significant relationship between human & technological capacities development and agribusiness.

Model Fitting Information in <Table 7>, describes the relationship between inadequate human & technological capacities development and agribusiness survival and reveals that probability of the model chi-square 240.897 was 0.000, less than the level of significance of 0.05 (i.e p<0.05) which means that the model is statistically significant to predict the dependent variable(monthly net profit).

In this survey, the model summary is given as follows:

Monthly net profit <$55 compared to >$140: For one unit increase in agribusiness operator with inadequate technological capacity whose monthly net profit is <$55 compared to >$140, the multinomial log-odds of business survival would increase by 0.517 unit while other variables are held constant. The variable was statistically significant at p<0.036, and the odds ratio from the result is 1.68 meaning the odd increase by 68% that is, a unit increase in agribusiness operator with inadequate technological capacity whose monthly net profit is <$55 increases the odds by 68% holding all other factors constant. The second variable of interest, holding other variables constant, one unit increase in agribusiness operator with inadequate human capacity development whose monthly net profit is <$55 compared to >$140, the log-odds of business survival would increase by 0.525 units. The result shows that its odd ratio is 1.69 which means that if all factors are held constant, as agribusiness operators without human capacity development whose monthly net profit of <$55 increases by one unit, the odd of making <$55 monthly net profit compared to >$140 increases 1.69 times (i.e. increases by 69%) compared to making >$140 monthly net profit. The variable was also statistically significant at p<0.027, implying that we would reject the null hypothesis.

Monthly net profit of $56-$85 compared to >$140. Factor 1, 2 and 3 were not statistically significant when the month net profit of agribusiness operators within the range of $56-$85 was compared with the reference category. However, the odd ratios show that a unit increase in agribusiness operator with inadequate technological and human capacity whose monthly net profit is within the range of $56-$85 increases the odds by 55% and 52% respectively compared to making >$140 while holding all other factors constant.

Monthly net profit of $86 - $111 compared to >$140. Factor 1 is statistically significant at p<0.001, factor 2 at p<0.049, this also means that we can reject the null hypothesis. The odds for factor 1 and factor 2 are 1.14 and 1.25, meaning that if other factors are held constant, a unit increase in agribusiness operators with inadequate technological and human capacity whose monthly net profit falls within the range of $86 - $111 increases the odds by 14% and 25% respectively compared to making >$140. Factor 3 was also statistically significant at p<0.018. For the coefficient for factor 3 is 0.608, this means that for one unit agribusiness operators with inadequate capital whose monthly net profit is within $86 - $111 compared to monthly net profit of >$140, holding other factors constant, the multinomial log-odds for making <$55 is expected to increase by 0.608. This literarily means that there is a 61% probability of earning monthly net profit within the range of $86 - $111 rather than the monthly net profit of >$140 due to inadequate capital to effectively run the firm.

Monthly net profit within the range of $112-$140 compared to >$140: The result indicates that factor 2 was statistically significant at p<0.035. The odd ratio also shows that inadequate human capacity development gross affects agribusiness. Factor 4 in all the categories of monthly net profit, factor 3 in the categories of <$55, $112-$140, factor 1 in the category of $112-$140 showed negative coefficients. This means that these factors when compared with the reference category relatively have less effect on agribusiness and high effect in the categories where its coefficients are positive. The implication of the result is that there is less likely effect of the dependent variable, unlike inadequate technological and human capacities development that strongly shows the effect on agribusiness. This result also reflects the result of the descriptive statistics which indicated that a high percentage of agribusiness firms is to a great and very great extent are affected inadequate human capacity development, inadequate technological capacity development, and inadequate capital among other factors. In addition, the result shows that the inadequate technological capacity development was due to some constraint faced by agribusiness firms such as Business environment, high cost of purchase/maintenance, chances of success if adopted. The results are inconsonant with the study of ChegeNg’ang’a and Gichira (2017) which revealed that technological capacities and human resource capacities affected the growth of Agribusiness Micro and Small Enterprises in Embu County. The result is also in line with the findings of Suresh et al (2016) which identified that human and technological capacities are needed for agribusiness development and management in sub-Saharan Africa at the individual, organization, and policy process levels.

CONCLUSIONS

The results of the factor analysis in this paper shows, factor 1 conclude: production, storage, packaging, processing, technology, post-harvest handling. Factor 2 conclude: marketing skills, bood and record-keeping, business planning/ development, finance and credit management. Factor 3 conclude: finance and credit management, income, investment analysis. Factor 4 conclude: transportation, supply.

The totality of the multinomial logistic results shows the percentage decrease in the odds of the independent variables with respect to the different categories of monthly net profits (dependent variable). The result revealed that the odd or probability of earning low net profit by firms with inadequate technological and human capacity development in the study areas is high, which means that inadequate human and technological capacities negatively affect agribusiness negatively. This implies that the increase in the number of agribusinesses with inadequate technological and human capacity development will continue to cause a decrease in their income level and as such, the chances of agribusiness firms will continue to decline causing little or no competition in the agricultural sector. The lowest monthly net profit category indicated higher odds or probability; implying that the increase in the number of agribusiness firms with inadequate technological and human capacity development will continue to cause a decrease in their income level and as such, the chances of agribusiness will continue to decline.

적 요

본 연구에서는 포트하코트 시와 나이지리아 리버스주 오비 오/아크박 지역의 인력과 기술력 부족이 농업 종합 기업 발전 에 영향을 주는지 검토하고, 또한 이외 어떠한 요인이 농업 종합 기업에 영향을 미치는지 분석을 해보았다. 데이터는 두 지방 정부에서 300개의 농업기업에 대해 확보한 자료이다. 분 석방법은 인구적 통계, 요인분석 및 다항로지스틱회귀분석을 이용하였다. 다항로지스틱 분석결과 유의성이 5% 이내이며, 이는 인력과 기술력 부족이 농업 종합 기업 발전에 영향을 주 는 것을 의미한다. 이에 농업 종합 기업의 수익을 높이기 위 해 기업 내부의 관리를 철저히 해야되고 내부 인원의 협작 및 기술능력을 강화시켜야 한다. 또한 과학기술혁신을 통해 기업 의 개혁을 촉진해야 한다.